Main Content

Data Clustering

Find clusters in input/output data using fuzzy c-means or subtractive clustering

The purpose of clustering is to identify natural groupings from a large data set to produce a concise representation of the data. You can use Fuzzy Logic Toolbox™ software to identify clusters within input/output training data using either fuzzy c-means or subtractive clustering. Also, you can use the resulting cluster information to generate a fuzzy inference system to model the data behavior. For more information, see Fuzzy Clustering.


fcmFuzzy c-means clustering
fcmOptionsFCM clustering options (Since R2023a)
subclustFind cluster centers using subtractive clustering
findclusterOpen clustering tool